\Header{lqs}{Resistant Regression}
\alias{lmsreg}{lqs}
\methalias{lqs.default}{lqs}
\methalias{lqs.formula}{lqs}
\alias{ltsreg}{lqs}
\keyword{models}{lqs}
\keyword{robust}{lqs}
\begin{Description}\relax
Fit a regression to the \emph{good} points in the dataset, thereby
achieving a regression estimator with a high breakdown point.
\code{lmsreg} and \code{ltsreg} are compatibility wrappers.\end{Description}
\begin{Usage}
\begin{verbatim}
lqs(x, ...)

## S3 method for class 'formula':
lqs(formula, data, ...,
    method = c("lts", "lqs", "lms", "S", "model.frame"),
    subset, na.action = na.fail, model = TRUE,
    x.ret = FALSE, y.ret = FALSE, contrasts = NULL)

## Default S3 method:
lqs(x, y, intercept = TRUE, method = c("lts", "lqs", "lms", "S"),
    quantile, control = lqs.control(...), k0 = 1.548, seed, ...)

lmsreg(...)
ltsreg(...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{formula}] a formula of the form \code{y \textasciitilde{} x1 + x2 + ...}.
\item[\code{data}] data frame from which variables specified in
\code{formula} are preferentially to be taken.
\item[\code{subset}] an index vector specifying the cases to be used in
fitting. (NOTE: If given, this argument must be named exactly.)
\item[\code{na.action}] function to specify the action to be taken if
\code{NA}s are found.  The default action is for the procedure to
fail.  Alternatives include \code{\Link{na.omit}} and
\code{\Link{na.exclude}}, which lead to omission of
cases with missing values on any required variable.  (NOTE: If
given, this argument must be named exactly.)

\item[\code{model, x.ret, y.ret}] logical. If \code{TRUE} the model frame,
the model matrix and the response are returned, respectively.
\item[\code{contrasts}] an optional list.  See the \code{contrasts.arg}
of \code{\Link{model.matrix.default}}.
\item[\code{x}] a matrix or data frame containing the explanatory variables.
\item[\code{y}] the response: a vector of length the number of rows of \code{x}.
\item[\code{intercept}] should the model include an intercept?
\item[\code{method}] the method to be used. \code{model.frame} returns the model frame: for the
others see the \code{Details} section. Using \code{lmsreg} or
\code{ltsreg} forces \code{"lms"} and \code{"lts"} respectively.

\item[\code{quantile}] the quantile to be used: see \code{Details}. This is over-ridden if
\code{method = "lms"}.

\item[\code{control}] additional control items: see \code{Details}.
\item[\code{k0}] the cutoff / tuning constant used for \eqn{\chi()}{chi()}
and \eqn{\psi()}{psi()} functions when \code{method = "S"}, currently
corresponding to Tukey's \dQuote{biweight}.
\item[\code{seed}] the seed to be used for random sampling: see \code{.Random.seed}. The
current value of \code{.Random.seed} will be preserved if it is set..

\item[\code{...}] arguments to be passed to \code{lqs.default} or
\code{lqs.control}, see \code{control} above and \code{Details}.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
Suppose there are \code{n} data points and \code{p} regressors,
including any intercept.

The first three methods minimize some function of the sorted squared
residuals. For methods \code{"lqs"} and \code{"lms"} is the
\code{quantile} squared residual, and for \code{"lts"} it is the sum
of the \code{quantile} smallest squared residuals. \code{"lqs"} and
\code{"lms"} differ in the defaults for \code{quantile}, which are
\code{floor((n+p+1)/2)} and \code{floor((n+1)/2)} respectively.
For \code{"lts"} the default is \code{floor(n/2) + floor((p+1)/2)}.

The \code{"S"} estimation method solves for the scale \code{s}
such that the average of a function chi of the residuals divided
by \code{s} is equal to a given constant.

The \code{control} argument is a list with components
\describe{
\item[\code{psamp}:] the size of each sample. Defaults to \code{p}.
\item[\code{nsamp}:] the number of samples or \code{"best"} (the
default) or \code{"exact"} or \code{"sample"}.
If \code{"sample"} the number chosen is \code{min(5*p, 3000)},
taken from Rousseeuw and Hubert (1997).
If \code{"best"} exhaustive enumeration is done up to 5000 samples;
if \code{"exact"} exhaustive enumeration will be attempted however
many samples are needed.
\item[\code{adjust}:] should the intercept be optimized for each
sample?  Defaults to \code{TRUE}.
}\end{Details}
\begin{Value}
An object of class \code{"lqs"}.  This is a list with components
\begin{ldescription}
\item[\code{crit}] the value of the criterion for the best solution found, in
the case of \code{method == "S"} before IWLS refinement.
\item[\code{sing}] character. A message about the number of samples which
resulted in singular fits.
\item[\code{coefficients}] of the fitted linear model
\item[\code{bestone}] the indices of those points fitted by the best sample
found (prior to adjustment of the intercept, if requested).
\item[\code{fitted.values}] the fitted values.
\item[\code{residuals}] the residuals.
\item[\code{scale}] estimate(s) of the scale of the error. The first is based
on the fit criterion.  The second (not present for \code{method ==
      "S"}) is based on the variance of those residuals whose absolute
value is less than 2.5 times the initial estimate.
\end{ldescription}
\end{Value}
\begin{Note}\relax
There seems no reason other than historical to use the \code{lms} and
\code{lqs} options.  LMS estimation is of low efficiency (converging
at rate \eqn{n^{-1/3}}{}) whereas LTS has the same asymptotic efficiency
as an M estimator with trimming at the quartiles (Marazzi, 1993, p.201).
LQS and LTS have the same maximal breakdown value of
\code{(floor((n-p)/2) + 1)/n} attained if
\code{floor((n+p)/2) <= quantile <= floor((n+p+1)/2)}.
The only drawback mentioned of LTS is greater computation, as a sort
was thought to be required (Marazzi, 1993, p.201) but this is not
true as a partial sort can be used (and is used in this implementation).

Adjusting the intercept for each trial fit does need the residuals to
be sorted, and may be significant extra computation if \code{n} is large
and \code{p} small.

Opinions differ over the choice of \code{psamp}.  Rousseeuw and Hubert
(1997) only consider p; Marazzi (1993) recommends p+1 and suggests
that more samples are better than adjustment for a given computational
limit.

The computations are exact for a model with just an intercept and
adjustment, and for LQS for a model with an intercept plus one
regressor and exhaustive search with adjustment. For all other cases
the minimization is only known to be approximate.\end{Note}
\begin{Author}\relax
B. D. Ripley\end{Author}
\begin{References}\relax
P. J. Rousseeuw and A. M. Leroy (1987)
\emph{Robust Regression and Outlier Detection.}
Wiley.

A. Marazzi (1993)
\emph{Algorithms, Routines and S Functions for Robust Statistics.}
Wadsworth and Brooks/Cole.

P. Rousseeuw and M. Hubert (1997) Recent developments in PROGRESS. In
\emph{L1-Statistical Procedures and Related Topics},
ed Y. Dodge, IMS Lecture Notes volume \bold{31}, pp. 201--214.\end{References}
\begin{SeeAlso}\relax
\code{\Link{predict.lqs}}\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
data(stackloss)
set.seed(123)
lqs(stack.loss ~ ., data = stackloss)
lqs(stack.loss ~ ., data = stackloss, method = "S", nsamp = "exact")
\end{ExampleCode}
\end{Examples}

